EPISODE · Dec 19, 2016 · 1H 2M
#31: Data Center Workload Analysis
from Storage Developer Conference · host Peter Murray, Principal System Engineer, Virtual Instruments
For years, enterprises have desired a way to ensure that an application will operate properly at scale on a particular storage array and network infrastructure, and increasingly, ensure that multiple applications can safely run at capacity on an all-flash or hybrid architecture. Companies need an approach that enables users to reference detailed workload analysis profiles as they build and share workload models, and simulate performance across a variety of vendors, products and technologies. Learning Objectives: 1) How to determine what the traffic related to an enterprise application implementation looks like from the perspective of the network infrastructure between application servers and storage arrays; 2) How the application I/O workload can be modeled to accurately represent the application in production using storage analytics or vendor performance statistics to generate a synthetic workload model; 3) How a statistical application model can be used to validate both array and network performance at varying traffic levels and rates to rapidly test if/then scenarios; 4) How multiple application models can be combined to determine appropriate throughput and response time levels on a given array; 5) How using these models can enable engineers and architects to cost-effectively test and confidently deploy new networked storage infrastructure.
What this episode covers
For years, enterprises have desired a way to ensure that an application will operate properly at scale on a particular storage array and network infrastructure, and increasingly, ensure that multiple applications can safely run at capacity on an all-flash or hybrid architecture. Companies need an approach that enables users to reference detailed workload analysis profiles as they build and share workload models, and simulate performance across a variety of vendors, products and technologies. Learning Objectives: 1) How to determine what the traffic related to an enterprise application implementation looks like from the perspective of the network infrastructure between application servers and storage arrays; 2) How the application I/O workload can be modeled to accurately represent the application in production using storage analytics or vendor performance statistics to generate a synthetic workload model; 3) How a statistical application model can be used to validate both array and network performance at varying traffic levels and rates to rapidly test if/then scenarios; 4) How multiple application models can be combined to determine appropriate throughput and response time levels on a given array; 5) How using these models can enable engineers and architects to cost-effectively test and confidently deploy new networked storage infrastructure.
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#31: Data Center Workload Analysis
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